The principal selection criteria for investments that will constitute an investment portfolio are performance and diversification.
Although there is no guarantee that past performance patterns will be repeated in the future, it is considered desirable to avoid investments the historical performance of which has failed to meet some minimum criteria or has been unstable or inconsistent.
In any market conditions we can expect that some investments will perform well and others will perform badly. The concept of risk diversification is to construct a multi-investment portfolio so that under all market conditions some combination of good performers will always offset the under-performers and the portfolio consistently achieves its objectives.
Performance
Quantitative performance data tends to begin by showing average return based on different variations of the underlying data, e.g., total return, load-adjusted return or tax-adjusted return. The data may also include other standard performance measures such as volatility, semi-variance, drawdown, Sharpe ratio or Sortino ratio together with proprietary measures specific to the particular database provider. Perhaps the most widely recognized examples of the latter would be the Star Rating for mutual funds published by Chicago-based Morningstar, Inc. or the Timeliness Ranking for stocks published by New York-based Value Line
In the case of mutual funds and other collective investment programmes, a second set of performance data is based on the performance attribution and style analysis approach favoured by institutional investors. The goal of performance attribution and style analysis is to divide a fund manager's returns into two parts—style and skill. Style is the part of the returns that is attributable to market movements and is dominated by the asset class mix in a portfolio. Skill is the part unique to the manager and is usually associated with individual security selection decisions within each asset class.
This analysis is usually accomplished through the construction of regression-based models, an approach that has evolved from the pioneering work of William Sharpe who first developed the Capital Asset Pricing Model. The models try to measure the systematic, causal relationship between the price performance of a fund and the movement in one or more market indexes. The measure of a fund's systematic relationship with a market index is called its ‘Beta’ while that portion of a fund's return that has no systematic relationship to the specified market indexes is called its ‘Alpha’. Although theoretically this is not correct, Alpha is often interpreted as representing the skill of the manager and used to rank manager performance.
Most analytical software today calculates the average performance of an investment over a specific term, e.g., the most recent 1, 3, or 5 years, selected calendar years, or since inception. In addition, many analytical tools compare investments by showing how much $10,000 would have grown over a specific term. Most consumers believe that these simple averages and growth graphs reflect the results that would have been achieved for any shorter sub-period, or holding period, within the specified term. However, analysis by the inventor shows that this is often not the case, and the discrepancy can be very large. Thus, there is a need for a better measurement that can capture not only performance during a single term but also the consistency of performance for all holding periods within that term.
The quantitative criteria commonly used to compare performance are measured in many different units and the range of values can very greatly. For example, return and volatility are both measured in percentages, but returns can be positive or negative whereas volatility can only be non-negative. In contrast, Sharpe ratio and correlation are both measured in integers, but Sharpe ratio is unbounded, whereas correlation must always take a value between −1 and 1. Typically, many software applications for analyzing investments provide multiple fields with different performance measurements for comparison among investments, but offer no methodology or technical capability to combine multiple criteria into a single composite result. Where ranking capability on single criteria is provided, the most common form of ranking is percentiles or simple ordinal rank. The limitation of this measurement is that it provides no information about the scale of difference in the relative performance of the ranked investments. Thus, there is a need for better investment analysis tools including a single score that allows easy comparison of investment performance.
Diversification
Mutual funds are most commonly grouped by applying a pre-defined classification system to their underlying holdings. The classification systems are usually based on a combination of geography (US, Europe, Latin America, Pacific/Asia, Japan), sector (Communications, Financial, Health etc.) and style (large-cap, mid-cap, small-cap, value, growth, balanced) for equities and duration (long term, intermediate, short-term) or tax status (taxable, non-taxable) for bonds. Thus Morningstar Inc., mentioned above, defines four main groupings that are further subdivided into 48 categories. The Investment Funds Standards Committee of Canada defines five main grouping that are sub-divided into 33 categories.
Under the style analysis approach, the simplest form of regression model identifies the single index with which the fund's performance is most closely related (this is sometimes referred to as the ‘best-fit index’) and funds can be grouped based on this criterion.
The investment strategies pursued by most mutual funds and ‘traditional’ institutional investment management programmes are usually subject to restrictions on shorting securities or applying leverage and the investment manager is often constrained to buying and holding assets in a few well-defined asset classes. These buy-and-hold strategies lend themselves to the two principal grouping methods described above.
In recent years however there has been an explosion of investment in hedge funds that employ considerably more sophisticated and dynamic trading strategies in pursuit of absolute returns with no systematic relationship to the general market. These funds may employ a very wide range of techniques (including shorting and leverage), may trade in all markets (defined by asset type as well as geography) and use a diverse range of trading instruments (including futures, swaps, options and other financial derivative contracts).
Because time series of performance data is very limited, because these funds generally do not disclose detailed position information, and because of the dynamic nature and complexity of their trading strategies, traditional holdings-based or style analysis methods can not be extended to these funds.
(Extensive efforts are being made to apply style analysis methods to the performance of hedge funds but these efforts face many technical problems in the construction of appropriate indexes and as yet there are no generally accepted standards.)
A third grouping method has therefore been developed for this class of funds, based primarily on a description of the manager's strategy rather than the characteristics of the fund's holdings. Examples or of such descriptors are as follows: Long/Short Equity Hedge; Short-Only; Event Driven; Distressed Situations; Merger Arbitrage; Convertible Arbitrage; Fixed Income Arbitrage; Capital Structure Arbitrage; Credit Arbitrage; Mortgage-Backed Securities; Market Neutral; Relative Value; Global Macro; Emerging Markets; and Currency.
Many of these descriptors do not have standard definitions and many funds employ multiple strategies in multiple markets, making it difficult to assign them to a single category. Therefore, although a strategy-labeling approach is widely used the resulting classification systems have not yet coalesced into a generally accepted common format.